An EOQ Based Multi-Storage Location of Spare Part Inventories: A Case Study

2013 ◽  
Vol 315 ◽  
pp. 733-738 ◽  
Author(s):  
Noor Ajian Mohd-Lair ◽  
Chuan Kian Pang ◽  
Willey Y.H. Liew ◽  
Hardy Semui ◽  
Loh Zhia Yew
Keyword(s):  
Inventory Management ◽  
Spare Part ◽  
Spare Parts ◽  
Inventory Level ◽  
Management Technique ◽  
Inventory Management System ◽  
Spare Parts Inventory ◽  
Storage Location ◽  
Manual Recording

Spare parts inventory management is very important to ensure smooth operation of maintenance department. The main objectives of inventory management of spare parts are to ensure the availability of spares and materials for the maintenance tasks and increase the productivity of the maintenance department. This research centred on the development of the Computerised Inventory Management System (CIMS) for the maintenance team at Weida Integrated Industries Sdn. Bhd. The inventory management technique used to control the spare parts inventory in this research was the basic Economic Order Quantity models (EOQ). However, the CIMS developed is unique as it has the ability in handling inventories in multiple-storage locations. The CIMS was written using the Visual Basic 2010 software. This CIMS has the abilities to keep records and process the spare parts information effectively and faster besides helping the user to perform spare parts ordering tasks compared to the current manual recording. In addition, the ordering quantity and frequency for the CIMS is determined through the EOQ technique. However, observation indicates that the overall average inventory level currently at the factory is lower than the expected overall average inventory level produced by the CIMS. This is due to the fact that the CIMS was unable to consider the opening stock in ordering the inventories. Therefore, further improvements are needed to optimize the performance of the system such as using the EOQ with the reorder point technique, the periodic or continuous review system.

A Risk Based Methodology for Spare Parts Inventory Optimisation

2008 ◽  
Author(s):  
Ujjwal R. Bharadwaj ◽  
Vadim V. Silberschmidt ◽  
John B. Wintle ◽  
Julian B. Speck
Keyword(s):  
Inventory Management ◽  
Optimal Level ◽  
Risk Profile ◽  
Spare Parts ◽  
Inventory Level ◽  
Machine Availability ◽  
Inventory Optimization ◽  
Spare Parts Inventory ◽  
Current Context ◽  
Capital Intensive

Spare parts inventories assist maintenance staff to keep equipment in operating condition. Thus the inventory level of spares has a direct bearing on machine availability, a factor that is increasingly important in capital-intensive industries. This paper presents a risk based approach for spare parts inventory optimization. At the outset, the paper highlights the unique features of maintenance inventories, such as spare parts inventories, compared to other inventories such as work-in-progress or finished product inventories. After a brief mention of the principles on which many of the current inventory management models are based and their limitations, the paper presents a risk-based methodology to spares inventory management. ‘Risk’ in the current context is the risk in monetary terms that arises when a component (spare) is not available on demand. It is the expected value of loss, i.e., the product of the likelihood of unavailability of the spare from the inventory and an estimate of the consequence(s) of that unavailability. Given a budgetary constraint and the risk profile of a number of spares, the model gives an optimal inventory of spares. By basing the inventory on the risk profile of spares, the model includes factors that are not normally considered in various other models. The ultimate aim of the methodology is to have an optimal level of spares inventory such that machine availability, to the extent it is dependent on the level of spares inventory, is maximized subject to constraints. The methodology is expected to benefit both, operational and financial managers.

Spare Parts Inventory Management System in a Service Sector Company

2021 ◽  
pp. 787-799
Author(s):  
Buse Atakay ◽  
Özge Onbaşılı ◽  
Simay Özcet ◽  
İrem Akbulak ◽  
Hatice Birce Cevher ◽  
...  
Keyword(s):  
Inventory Management ◽  
Management System ◽  
Service Sector ◽  
Spare Parts ◽  
Inventory Management System ◽  
Spare Parts Inventory

scholarly journals Joint Optimization of Preventive Maintenance and Spare Parts Inventory with Appointment Policy

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Jing Cai ◽  
Yibing Yin ◽  
Li Zhang ◽  
Xi Chen
Keyword(s):  
Inventory Management ◽  
Preventive Maintenance ◽  
Spare Parts ◽  
Combination Method ◽  
Joint Optimization ◽  
Inventory Level ◽  
Cost Rate ◽  
Proposed Model ◽  
Spare Parts Inventory ◽  
The Cost

Under the background of the wide application of condition-based maintenance (CBM) in maintenance practice, the joint optimization of maintenance and spare parts inventory is becoming a hot research to take full advantage of CBM and reduce the operational cost. In order to avoid both the high inventory level and the shortage of spare parts, an appointment policy of spare parts is first proposed based on the prediction of remaining useful lifetime, and then a corresponding joint optimization model of preventive maintenance and spare parts inventory is established. Due to the complexity of the model, the combination method of genetic algorithm and Monte Carlo is presented to get the optimal maximum inventory level, safety inventory level, potential failure threshold, and appointment threshold to minimize the cost rate. Finally, the proposed model is studied through a case study and compared with both the separate optimization and the joint optimization without appointment policy, and the results show that the proposed model is more effective. In addition, the sensitivity analysis shows that the proposed model is consistent with the actual situation of maintenance practices and inventory management.

scholarly journals LUMPY DEMAND FORECASTING USING LINEAR EXPONENTIAL SMOOTHING, ARTIFICIAL NEURAL NETWORK, AND BOOTSTRAP

2018 ◽  
Vol 10 (2) ◽  
pp. 107
Author(s):  
Sinta Rahmawidya Sulistyo ◽  
Alvian Jonathan Sutrisno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inventory Management ◽  
Spare Part ◽  
Service Level ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Spare Parts Inventory

Lumpy demand represents the circumstances when a demand for an item has a large proportion of periods having zero demand. This certain situation makes the time series methods might become inappropriate due to the model’s inability to capture the demand pattern. This research aims to compare several forecasting methods for lumpy demand that is represented by the demand of spare part. Three forecasting methods are chosen; Linear Exponential Smoothing (LES), Artificial Neural Network (ANN), and Bootstrap. The Mean Absolute Scaled Error (MASE) is used to measure the forecast performance. In order to gain more understanding on the effect of the forecasting method on spare parts inventory management, inventory simulation using oil and gas company’s data is then conducted. Two inventory parameters; average inventory and service level; are used to measure the performance. The result shows that ANN is found to be the best method for spare part forecasting with MASE of 0,761. From the inventory simulation, the appropriate forecasting method on spare parts inventory management is able to reduce average inventory by 11,9% and increase service level by 10,7%. This result justifies that selecting the appropriate forecasting method is one of the ways to achieve spare part inventory management’s goal.

Time-based responsive logistics for a maintenance service network

2019 ◽  
Vol 25 (4) ◽  
pp. 589-611 ◽  
Author(s):  
Ioannis Manikas ◽  
Balan Sundarakani ◽  
Vera Iakimenko
Keyword(s):  
Case Study Research ◽  
Spare Part ◽  
Service Level ◽  
Spare Parts ◽  
Retail Banking ◽  
Distribution Centers ◽  
Service Network ◽  
Content Type ◽  
Maintenance Service

PurposeThe purpose of this paper is to identify the main reasons for spare parts logistics failures and address logistics distribution design in order to achieve the desired level of after-sales maintenance service.Design/methodology/approachThis research is based on an empirical case study on a large corporation providing worldwide with retail banking hardware, software and services. The case study focuses on the automated teller machine (ATM) part of activities, with a focus on the spare parts distribution and after-sales service network in the Eastern Europe.FindingsThe proposed network solution of multiple distribution centers with short-cut distance saving approach will enable the case study company to redesign their spare part logistics architecture in order to achieve short response time. Research findings reveal possible spare parts delivery delays and thus the service-level agreement failures with clients in the case study company.Research limitations/implicationsThis research covers a particular supply chain environment and identified research gaps. It discusses a time-based responsive logistics problem and develops a conceptual framework that would help researchers to better understand logistics challenges of installed equipment maintenance and after-sales service.Originality/valueThis case study research shows the “big picture” of spare parts logistics challenges as vital part of installed equipment after-sales and maintenance service network, as well as emphasizes how the unique context of a market like Russian Federation can set-up a distribution network efficiently. Strategies applied to handle such service-level failures, reverse logistics aspects of repairable and non-repairable spare parts to such large ATM after-sales service network based on this longitudinal case offer value for similar scale companies.

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